The present invention relates to a terminal device such as a word processor, a personal computer, a work station, or a computer, and in particular to a customised personal terminal device so individualized as to deal with the feature of an individual user such as manipulation likes peculiar to the user, the user's voice and hand-writing characteristics, and a terminal device for making such a terminal device portable.
Conventional man-machine interfaces are described in
(1) "Machintosh manual", "Machintosh SE", Apple Corp., USA. PA1 (2) "Speech recognition using neural net discriminates 70% of consonants which have been recognized incorrectly heretofore", NE report, Nikkei Electronics, Nov. 14, 1988. PA1 (3) "Use of neural net in pattern processing, signal processing and knowledge processing", Nikkei Electronics, Aug. 10, 1988.
A terminal having the most advanced man-machine interface was heretofore Machintosh described in the aforementioned literature (1). As described in the manual, this terminal makes it possible to set basic manipulation environments such as kana (Japanese syllabary)/romaji (a method of writing Japanese in Roman characters) key setting, kanji (Chinese character) code inputting method, cursor flashing speed, mouse response speed, file display mode, speaker volume and printing and communication port setting so as to satisfy the user's likings. Further, it is also possible to carry the above described manipulation parameters by using a floppy disk as a medium and apply those parameters to another Machintosh terminal.
On the other hand, speech recognition using the neural net, recitation of text data, character recognition and other techniques are now being developed individually.
The recognition rate can be improved by using the neural net as described above. As reported in the aforementioned literature (2), for example, approximately 70% of consonants which have heretofore been recognized incorrectly can be correctly recognized.
Further, by using Neural net having a learning function, it becomes possible to make terminals learn chracteristics of recognition or text synthesis (recitation of text data by using synthesized speech). In NET talk described in the aforementioned literature (3), for example, the terminal learns a proper pronunciation method of text data by using a document containing approximately 1,000 words picked from a book for pronunciation practice of children, 20,000 words picked from a Webster dictionary, and proper phonetic symbols corresponding to them. As a result, accuracy of pronunciation amounts to 95% after 50 learning attempts.